Dynamics of Inter-community Spread of Covid-19

Dansu, Emmanuel Jesuyon; Ogunjo, Samuel

doi:10.1007/978-981-16-2450-6_18

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…The studies on COVID‐19 within Nigeria, as it relates to air pollution has been limited to it's role in reduction of air pollution (Fuwape et al., 2021 ; Olusola et al., 2021 ) and modeling (Dansu & Ogunjo, 2021 ). Because pollution sources vary across Nigeria (natural and anthropogenic), the potential linkages to COVID‐19 are likely to vary.…”

Section: Introductionmentioning

confidence: 99%

The Role of Meteorological Variables and Aerosols in the Transmission of COVID‐19 During Harmattan Season

et al. 2022

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The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), originating from the Wuhan region of China, has caused devastating damages across the world. The impact of SARS-CoV-2 (COVID-19) has been reported in global transportation (Abu-Rayash & Dincer, 2020) and power system (Gulati et al., 2021). The greatest impact has been on the global health system, overwhelming the health care facilities of many developed and developing countries (Armocida et al., 2020). COVID-19 infections could be asymptomatic, symptomatic or presymptomatic leading to severe forms which require intubation and can lead to death (Lee et al., 2020). For instance, older patients with more than one underlying medical condition (e.g., hypertension, obesity, diabetes, chronic kidney diseases etc.) are more predisposed to severe COVID-19 complications (Fang et al., 2020; R. T. CDC et al., 2020), although younger and healthier patients are more likely to respond faster to treatment (C. CDC et al., 2020;Wang et al., 2020). Multiple organ failure and cardiopulmonary complications, such as myopericarditis, pulmonary embolism and acute respiratory distress syndrome, represent some of the major complications of severe COVID-19. The COVID-19 outbreak has created a global health crisis. There are a total of 171,259,456 COVID-19 cases, 3,567,030 deaths worldwide as of 2 June 2021 (John Hopkins University, 2021) with Nigeria having the highest number of cases (166,534) and deaths (2,099) in West Africa. The first case of COVID-19 incidence was reported on 27 February 2020. Since then the reported confirmed cases increased considerably to about 1,350 cases before the end of the second month. Lagos State, Kano State and Federal Capital Territory (FCT) reported the highest number of cases in Nigeria. The Federal government of Nigeria and various State Governments imposed restrictive measures such suspension of all activities and religious gatherings, indefinite closure of public and private schools/institutions, extension of the travel ban to some countries, suspension of the

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Section: Introductionmentioning

confidence: 99%

The Role of Meteorological Variables and Aerosols in the Transmission of COVID‐19 During Harmattan Season

et al. 2022

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A tale of two countries: Optimal control and cost-effectiveness analysis of monkeypox disease in Germany and Nigeria

Akinyemi,

Idisi,

Rabiu

et al. 2023

Healthcare Analytics

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Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

2022

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The dynamical nature of COVID‐19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID‐19 cases based on past infections, (b) predict current COVID‐19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K‐nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k‐nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID‐19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID‐19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID‐19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.

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Dynamics of Inter-community Spread of Covid-19

Cited by 3 publications

References 41 publications

The Role of Meteorological Variables and Aerosols in the Transmission of COVID‐19 During Harmattan Season

The Role of Meteorological Variables and Aerosols in the Transmission of COVID‐19 During Harmattan Season

A tale of two countries: Optimal control and cost-effectiveness analysis of monkeypox disease in Germany and Nigeria

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

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